Take-Home Exercise 4

Author

Chai Zhixuan

Published

February 28, 2024

Modified

March 2, 2024

1 Overview

In this take-home exercise, I will be selecting one of the modules of my team’s project proposed Shiny application. I will be working on storyboard 3: Geospatial Visualisation.

2. Setting the Analytical Tools

pacman::p_load(sf,  readxl, tmap, tidyverse)

3. Data Preparation

3.1 The data

  • The first dataset will from Energy Market Authority. I am using the tidy data.

  • The second dataset will be Master Plan 2019 Subzone Boundary (Web) (i.e. MP14_SUBZONE_WEB_PL) in ESRI shapefile format. It can be downloaded at data.gov.sg This is a geospatial data. It consists of the geographical boundary of Singapore at the planning subzone level. The data is based on URA Master Plan 2019.

  • The third dataset will be population data. I will download from: https://tablebuilder.singstat.gov.sg/table/CT/8186#!

3.2 Data Import and Preparation

3.2.1 Importing Geospatial Data into R

mpsz <- st_read(dsn = "data/geospatial", 
                layer = "MPSZ-2019")
Reading layer `MPSZ-2019' from data source 
  `C:\czx0727\ISSS608-VAA\take_home_ex\take_home_ex04\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84
mpsz
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84
First 10 features:
                 SUBZONE_N SUBZONE_C       PLN_AREA_N PLN_AREA_C       REGION_N
1              MARINA EAST    MESZ01      MARINA EAST         ME CENTRAL REGION
2         INSTITUTION HILL    RVSZ05     RIVER VALLEY         RV CENTRAL REGION
3           ROBERTSON QUAY    SRSZ01  SINGAPORE RIVER         SR CENTRAL REGION
4  JURONG ISLAND AND BUKOM    WISZ01  WESTERN ISLANDS         WI    WEST REGION
5             FORT CANNING    MUSZ02           MUSEUM         MU CENTRAL REGION
6         MARINA EAST (MP)    MPSZ05    MARINE PARADE         MP CENTRAL REGION
7                   SUDONG    WISZ03  WESTERN ISLANDS         WI    WEST REGION
8                  SEMAKAU    WISZ02  WESTERN ISLANDS         WI    WEST REGION
9           SOUTHERN GROUP    SISZ02 SOUTHERN ISLANDS         SI CENTRAL REGION
10                 SENTOSA    SISZ01 SOUTHERN ISLANDS         SI CENTRAL REGION
   REGION_C                       geometry
1        CR MULTIPOLYGON (((103.8802 1....
2        CR MULTIPOLYGON (((103.8376 1....
3        CR MULTIPOLYGON (((103.8341 1....
4        WR MULTIPOLYGON (((103.7125 1....
5        CR MULTIPOLYGON (((103.8472 1....
6        CR MULTIPOLYGON (((103.8987 1....
7        WR MULTIPOLYGON (((103.7235 1....
8        WR MULTIPOLYGON (((103.76 1.21...
9        CR MULTIPOLYGON (((103.8591 1....
10       CR MULTIPOLYGON (((103.8232 1....

3.2.2 Importing Attribute Data into R

SES_2 <- read_excel("data/SES_Public_2023_tidy.xlsx", sheet = "T3.5")
str(SES_2)
tibble [70,706 × 6] (S3: tbl_df/tbl/data.frame)
 $ dwelling_type: chr [1:70706] "1-room / 2-room" "1-room / 2-room" "1-room / 2-room" "1-room / 2-room" ...
 $ year         : num [1:70706] 2005 2005 2005 2005 2005 ...
 $ month        : chr [1:70706] "1" "1" "1" "1" ...
 $ Region       : chr [1:70706] "Central Region" "Central Region" "Central Region" "Central Region" ...
 $ Description  : chr [1:70706] "Bishan" "Bukit Merah" "Central Region" "Geylang" ...
 $ kwh_per_acc  : num [1:70706] 105 141 136 148 116 ...

I will keep to 2023, and overall with annual data

SES_2_1 <- SES_2 %>%
  filter(year == 2023, month == "Annual", dwelling_type == "Overall", Description != "Central Region", Description != "North Region", Description != "North East Region",Description != "Overall", Description != "West Region")

Convert areas to upper case.

SES_2_1 <- SES_2_1 %>%
  mutate_at(.vars = vars(Description), 
          .funs = funs(toupper))

3.2.3 Creating a sf data frame from an aspatial data frame

mpsz_SES <- left_join(mpsz, SES_2_1,
                          by = c("PLN_AREA_N" = "Description"))

I will remove rows with NA.

mpsz_SES_filtered <- mpsz_SES %>%
  mutate(kwh_per_acc = coalesce(kwh_per_acc, 0),dwelling_type = coalesce(dwelling_type, "NA"))%>%
  select(SUBZONE_N, dwelling_type,kwh_per_acc, geometry)

I will save the file to rds

write_rds(mpsz_SES_filtered, "data/rds/mpsz_SES_filtered.rds")

I will import the rds file

mpsz_SES_filtered <- read_rds("data/rds/mpsz_SES_filtered.rds")

4 Selection of data visualisation techniques used

Since this is related to geospatial, choropleth map will be used. A key aspect will be the appropriate number of classes to be used.

4.1 Colour

In terms of colour, I should avoid multiple colours as it will cause confusion to readers. I should stick with a single hue (or a small set of closely related hues) and vary intensity from pale colours for low values to increasingly darker and brighter colours for high values.

4.2 Graph Title

Interesting insight from the graph can be put as a main title. The graph title can be interesting to grab the attention of readers.

4.3 Legend

Legend will need to be clear to indicate the range and what it entails.

5 Visualisation (Electricity)

tmap_mode("view")
tmap_options(check.and.fix = TRUE)
qtm(mpsz_SES_filtered, 
    fill = "kwh_per_acc",
    title = "Electricity Consumption") +
  tm_borders()
tmap_mode("plot")
tm_shape(mpsz_SES_filtered)+
  tm_fill("kwh_per_acc", 
          style = "quantile", 
          palette = "Blues",
          title = "Electricity Consumption in kwh_per_acc") +
  tm_layout(main.title = "Total Household Electricity Consumption by Household in 2023",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha =0.2) +
  tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and electricity data from Energy Marketing Authority (EMA)", 
             position = c("left", "bottom"))

6 Visualisation (Population)

The above data cleaning process and visualisation is for electricity data. I will do the same for popluation data

6.1 Data Wrangling

Manual cleaning is required in the excel as it consists on rows that will not be used.

6.1.1 Importing Attribute Data into R

popdata <- read_csv("data/respopagesextod2011to2020.csv")

I will group the population together by the region.

popdata2020 <- popdata %>%
  filter(Time == 2020) %>%
  group_by(PA, SZ)%>%
  summarise(`POP` = sum(`Pop`))

I will change region name to uppercase

popdata2020 <- popdata2020 %>%
  mutate_at(.vars = vars(PA, SZ), 
          .funs = funs(toupper))

I will join the data together

mpsz_pop2020 <- left_join(mpsz, popdata2020,
                          by = c("SUBZONE_N" = "SZ"))

I will write the results to rds

write_rds(mpsz_pop2020, "data/rds/mpszpop2020.rds")

I will import the rds

mpsz_pop2020 <- read_rds("data/rds/mpszpop2020.rds")

6.2 Plotting of choropleth map

tmap_mode("view")
tmap_options(check.and.fix = TRUE)
qtm(mpsz_pop2020, 
    fill = "POP",
    title = "Singapore's population in 2020") +
  tm_borders()
tmap_mode("plot")
tm_shape(mpsz_pop2020)+
  tm_fill("POP", 
          style = "quantile", 
          palette = "Blues",
          title = "Singapore Population") +
  tm_layout(main.title = "Singapore's population by region in 2020",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha =0.2) +
  tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and Population data from Department of Statistics", 
             position = c("left", "bottom"))

I will plot the 2 maps side by side now

elecmap <- tm_shape(mpsz_SES_filtered)+
  tm_fill("kwh_per_acc", 
          style = "quantile", 
          palette = "Blues",
          title = "Electricity Consumption in kwh_per_acc") +
  tm_layout(main.title = "Total Household Electricity Consumption by Household in 2023",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha =0.2) +
  tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and electricity data from Energy Marketing Authority (EMA)", 
             position = c("left", "bottom"))

popmap <- tm_shape(mpsz_pop2020)+
  tm_fill("POP", 
          style = "quantile", 
          palette = "Blues",
          title = "Singapore Population") +
  tm_layout(main.title = "Singapore's population by region in 2020",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha =0.2) +
  tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and Population data from Department of Statistics", 
             position = c("left", "bottom"))

tmap_arrange(elecmap, popmap, asp=1, ncol=2)

tmap_mode("view")
elecmap <- tm_shape(mpsz_SES_filtered)+
  tm_fill("kwh_per_acc", 
          style = "quantile", 
          palette = "Blues",
          title = "Electricity Consumption in kwh_per_acc") +
  tm_layout(main.title = "Total Household Electricity Consumption by Household in 2023",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha =0.2) +
  tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and electricity data from Energy Marketing Authority (EMA)", 
             position = c("left", "bottom"))

popmap <- tm_shape(mpsz_pop2020)+
  tm_fill("POP", 
          style = "quantile", 
          palette = "Blues",
          title = "Singapore Population") +
  tm_layout(main.title = "Singapore's population by region in 2020",
            main.title.position = "center",
            main.title.size = 1.2,
            legend.height = 0.45, 
            legend.width = 0.35,
            frame = TRUE) +
  tm_borders(alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  tm_scale_bar() +
  tm_grid(alpha =0.2) +
  tm_credits("Source: Singapore Residents by Planning Area / Subzone, Age Group, Sex and Type of Dwelling from Department of Statistics)", 
             position = c("left", "bottom"))

tmap_arrange(elecmap, popmap, asp=1, ncol=2)
tmap_mode("plot")

7. UI design

8. R Shiny